The whale optimization algorithm (WOA) is a powerful swarm intelligence method which has been widely used in various fields such as parameter identification of solar cells and PV modules. In order to better balance the exploration and exploitation of WOA, we propose a novel modified WOA (MWOA) in which both the mutation strategy based on Levy flight and a local search mechanism of pattern search are introduced. On the one hand, Levy flight can make the algorithm get rid of the local optimum and avoid stagnation; thus, it is able to prevent the algorithm from losing diversity and to increase the global search capability. On the other hand, pattern search, a direct search method, has not only high convergence rate but also good stability, which can boost the local optimization ability of the WOA. Therefore, the combination of these two mechanisms can greatly improve the capability of WOA to obtain the best solution. In addition, MWOA may be employed to estimate parameters in single diode model (SDM), double diode model (DDM), and PV modules and to identify unknown parameters of two different types of PV modules under diverse light irradiance and temperature conditions. The analytical results demonstrate the validity and the practicality of MWOA for estimating parameters of solar cells and PV modules.

In recent years, fog-based vehicular ad-hoc networks (VANETs) has become a hot research topic. Due to the inherent insecurity of open wireless channels between vehicles and fog nodes, establishing session keys through authenticated key agreement (AKA) protocols is critically important for securing communications. However, existing AKA schemes face several critical challenges: (1) When a large number of vehicles initiate AKA requests within a short time window, existing schemes that process requests one by one individually incur severe signaling congestion, resulting in significant quality of service degradation. (2) Many AKA schemes incur excessive computational and communication overhead due to the adoption of computationally intensive cryptographic primitives (e.g., bilinear pairings and scalar multiplications on elliptic curve groups) and unreasonable design choices, making them unsuitable for the low-latency requirements of VANETs. To address these issues, we propose a lightweight batch AKA scheme based on fog computing. In our scheme, when a group of vehicles requests AKA sessions with the same fog node within the set time interval, the fog node aggregates these requests and, with assistance from the traffic control center, establishes session keys for all vehicles by a round of operations. It has significantly reduced the operational complexity of the entire system. Moreover, our scheme employs Lagrange interpolation and lightweight cryptographic tools, thereby significantly reducing both computational and communication overhead. Additionally, our scheme supports conditional privacy preservation and includes a revocation mechanism for malicious vehicles. Security analysis demonstrates that the proposed scheme meets the security and privacy requirements of VANETs. Performance evaluation indicates that our scheme outperforms existing state-of-the-art solutions in terms of efficiency.

Retrieval-augmented question answering commonly constructs the final context by directly truncating an item-level candidate ranking, but this strategy does not explicitly control evidence redundancy, complementarity, or evidence-chain completeness. We propose MORSE (Multi-Objective Retrieval Subset Evolution), a training-free post-retrieval method for selecting an evidence subset under a fixed context budget. MORSE constructs a question-adaptive search domain, organizes selected passages into functional core and support groups, and evaluates candidate subsets through three objectives: core-evidence quality, support complementarity, and package efficiency. NSGA-II searches for non-dominated evidence packages, while a conservative gate replaces the upstream ranking prefix only when protected quantities remain within predefined margins and at least one improvement is obtained. We evaluate MORSE on SQuAD v1.1, HotpotQA, and TriviaQA against direct Top-5 selection, cross-encoder reranking, MMR, submodular selection, and DPP. Compared with Top-5, MORSE yields higher mean F1 across all three datasets under both downstream QA models. In the SQuAD candidate source analysis, Hybrid+CE+MORSE obtains the strongest overall configuration, reaching 95.72 Recall and 88.54 MRR, with F1 scores of 69.96 for Qwen2.5:7B and 77.40 for Llama3.1:8B. The Hybrid Top-100 ablation identifies the core-quality pathway and conservative gate as the most influential components. Under the primary Top-100 setting, MORSE uses an average active search pool of 40.33 candidates and requires 120.76 ms per question. These results indicate that post-retrieval evidence-package optimization can complement upstream retrieval and reranking, particularly when useful complementary evidence remains distributed beyond the leading candidate prefix.